During character recognition, character feature extraction is a crucial step. The feature extraction may be regarded as extraction for key information of original characters, thus the features should reflect differences among different characters as far as possible. In other words, differences between different types of character features should keep as far as possible. If features are selected inappropriately, a good recognition rate cannot be reached no matter what kind of classier is selected.
In general, to recognize a character, after edge of the character or skeleton of the character is extracted, features are extracted from the edge of the character or the skeleton of the character. In existing methods, when a point of the edge or the skeleton is described, only information about the current point itself is considered. For example, (x, y, f (x, y)) is used as a feature of the point of the edge or the skeleton, where x, y represent coordinates of a point P, and f (x, y) represents a brightness value. In this way, the extracted features are local. If two characters have similar local shapes (such as Q and O), features extracted form the two characters are highly similar, which makes it difficult to distinguish the two characters, and a recognition rate is poor.
Hence, it is very important to provide a new feature extraction method using another idea to describe points of the edge or skeleton of a character, so as to further distinguish character features, thereby improving a character recognition rate.